






"""simple docstring"""


import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__SCREAMING_SNAKE_CASE   =   logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
		import torch_xla.debug.metrics as met




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	Union[str, Any]     , *UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Optional[Any]=None     , UpperCAmelCase							:	Tuple=None     , UpperCAmelCase							:	str=None     , **UpperCAmelCase							:	Optional[int]   )	->       int:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			eval_examples
											lowerCAmelCase   :Optional[Any]						=			post_process_function
											lowerCAmelCase   :int						=			quant_trainer_args
											lowerCAmelCase   :int						=			128  # default number of calibration samples
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	str=None   )	->       List[str]:
											if calib_dataset is None and self.calib_dataset is None:
																	raise ValueError('Trainer: calibration requires an calib_dataset.'   )
											lowerCAmelCase   :Union[str, Any]						=			calib_dataset if calib_dataset is not None else self.calib_dataset

											lowerCAmelCase   :Tuple						=			self._remove_unused_columns(UpperCAmelCase     , description='Calibration'   )

											return DataLoader(
											    UpperCAmelCase     , batch_size=self.args.eval_batch_size     , collate_fn=self.data_collator     , drop_last=self.args.dataloader_drop_last     , num_workers=self.args.dataloader_num_workers     , pin_memory=self.args.dataloader_pin_memory     , shuffle=UpperCAmelCase     , )
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	str=None   )	->       int:
											lowerCAmelCase   :List[str]						=			self.train_dataset if calib_dataset is None else calib_dataset
											lowerCAmelCase   :Dict						=			self.get_calib_dataloader(UpperCAmelCase   )

											lowerCAmelCase   :Union[str, Any]						=			self.model
											quant_trainer.configure_model(UpperCAmelCase     , self.quant_trainer_args     , calib=UpperCAmelCase   )
											model.eval()
											quant_trainer.enable_calibration(UpperCAmelCase   )

											logger.info('***** Running calibration *****'   )
											logger.info(f"""  Num examples = {self.calib_num}"""   )
											logger.info(f"""  Batch size = {calib_dataloader.batch_size}"""   )

											for step, inputs in enumerate(UpperCAmelCase   ):
																	# Prediction step
																	lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			self.prediction_step(UpperCAmelCase     , UpperCAmelCase     , prediction_loss_only=UpperCAmelCase   )
																	if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																							break

											quant_trainer.finish_calibration(UpperCAmelCase     , self.quant_trainer_args   )
											lowerCAmelCase   :Any						=			model
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Any=None     , UpperCAmelCase							:	List[str]=None     , UpperCAmelCase							:	Optional[int]=None     , UpperCAmelCase							:	str = "eval"   )	->       str:
											lowerCAmelCase   :Optional[Any]						=			self.eval_dataset if eval_dataset is None else eval_dataset
											lowerCAmelCase   :Any						=			self.get_eval_dataloader(UpperCAmelCase   )
											lowerCAmelCase   :str						=			self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											lowerCAmelCase   :List[Any]						=			self.compute_metrics
											lowerCAmelCase   :List[Any]						=			None
											lowerCAmelCase   :Any						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowerCAmelCase   :List[Any]						=			eval_loop(
																	    UpperCAmelCase     , description='Evaluation'     , prediction_loss_only=True if compute_metrics is None else None     , ignore_keys=UpperCAmelCase     , )
											finally:
																	lowerCAmelCase   :Any						=			compute_metrics

											if self.post_process_function is not None and self.compute_metrics is not None:
																	lowerCAmelCase   :Optional[int]						=			self.post_process_function(UpperCAmelCase     , UpperCAmelCase     , output.predictions   )
																	lowerCAmelCase   :Any						=			self.compute_metrics(UpperCAmelCase   )

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()   ):
																							if not key.startswith(f"""{metric_key_prefix}_"""   ):
																													lowerCAmelCase   :int						=			metrics.pop(UpperCAmelCase   )

																	self.log(UpperCAmelCase   )
											else:
																	lowerCAmelCase   :Any						=			{}

											if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()   )

											lowerCAmelCase   :Optional[int]						=			self.callback_handler.on_evaluate(self.args     , self.state     , self.control     , UpperCAmelCase   )
											return metrics
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	str     , UpperCAmelCase							:	Tuple=None     , UpperCAmelCase							:	str = "test"   )	->       Any:
											lowerCAmelCase   :List[str]						=			self.get_test_dataloader(UpperCAmelCase   )

											# Temporarily disable metric computation, we will do it in the loop here.
											lowerCAmelCase   :List[str]						=			self.compute_metrics
											lowerCAmelCase   :Tuple						=			None
											lowerCAmelCase   :Dict						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																	lowerCAmelCase   :int						=			eval_loop(
																	    UpperCAmelCase     , description='Prediction'     , prediction_loss_only=True if compute_metrics is None else None     , ignore_keys=UpperCAmelCase     , )
											finally:
																	lowerCAmelCase   :Tuple						=			compute_metrics

											if self.post_process_function is None or self.compute_metrics is None:
																	return output

											lowerCAmelCase   :str						=			self.post_process_function(UpperCAmelCase     , UpperCAmelCase     , output.predictions     , 'predict'   )
											lowerCAmelCase   :List[str]						=			self.compute_metrics(UpperCAmelCase   )

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()   ):
																	if not key.startswith(f"""{metric_key_prefix}_"""   ):
																							lowerCAmelCase   :Tuple						=			metrics.pop(UpperCAmelCase   )

											return PredictionOutput(predictions=predictions.predictions     , label_ids=predictions.label_ids     , metrics=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Any="./"   )	->       str:
											lowerCAmelCase   :Optional[Any]						=			self.eval_dataset
											lowerCAmelCase   :Tuple						=			self.get_eval_dataloader(UpperCAmelCase   )

											lowerCAmelCase   :Dict						=			next(iter(UpperCAmelCase   )   )

											# saving device - to make it consistent
											lowerCAmelCase   :List[Any]						=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'   )

											# convert to tuple
											lowerCAmelCase   :int						=			tuple(v.to(UpperCAmelCase   ) for k, v in batch.items()   )

											logger.info('Converting model to be onnx compatible'   )
											from pytorch_quantization.nn import TensorQuantizer

											lowerCAmelCase   :str						=			True

											lowerCAmelCase   :Dict						=			self.model.to(UpperCAmelCase   )

											model.eval()
											model.float()

											lowerCAmelCase   :Optional[Any]						=			model.module if hasattr(UpperCAmelCase     , 'module'   ) else model
											quant_trainer.configure_model(UpperCAmelCase     , self.quant_trainer_args   )

											lowerCAmelCase   :int						=			os.path.join(UpperCAmelCase     , 'model.onnx'   )
											logger.info(f"""exporting model to {output_model_file}"""   )

											lowerCAmelCase   :Optional[Any]						=			{0: 'batch_size', 1: 'seq_len'}

											torch.onnx.export(
											    UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , export_params=UpperCAmelCase     , opset_version=13     , do_constant_folding=UpperCAmelCase     , input_names=['input_ids', 'attention_mask', 'token_type_ids']     , output_names=['output_start_logits', 'output_end_logits']     , dynamic_axes={
											        'input_ids': axes,
											        'attention_mask': axes,
											        'token_type_ids': axes,
											        'output_start_logits': axes,
											        'output_end_logits': axes,
											    }     , verbose=UpperCAmelCase     , )
											logger.info('onnx export finished'   )







"""simple docstring"""


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__SCREAMING_SNAKE_CASE   =   {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   'hopper-medium-v2'
		__SCREAMING_SNAKE_CASE   =   gym.make(env_name)

		__SCREAMING_SNAKE_CASE   =   ValueGuidedRLPipeline.from_pretrained(
		    'bglick13/hopper-medium-v2-value-function-hor32',
		    env=env,
		)

		env.seed(0)
		__SCREAMING_SNAKE_CASE   =   env.reset()
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   0
		__SCREAMING_SNAKE_CASE   =   10_00
		__SCREAMING_SNAKE_CASE   =   [obs.copy()]
		try:
				for t in tqdm.tqdm(range(T)):
						# call the policy
						__SCREAMING_SNAKE_CASE   =   pipeline(obs, planning_horizon=32)

						# execute action in environment
						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   env.step(denorm_actions)
						__SCREAMING_SNAKE_CASE   =   env.get_normalized_score(total_reward)

						# update return
						total_reward += reward
						total_score += score
						print(
						    F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
						    F""" {total_score}"""
						)

						# save observations for rendering
						rollout.append(next_observation.copy())

						__SCREAMING_SNAKE_CASE   =   next_observation
		except KeyboardInterrupt:
				pass

		print(F"""Total reward: {total_reward}""")